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Record W4414048577 · doi:10.1101/2025.09.04.674151

Post-transcriptional Regulation Coordinating Transcription and Translation During Circadian Oscillation and Stress Recovery in Plants

2025· preprint· en· W4414048577 on OpenAlexaff
Pengfei Xu, You Wu, Qihui Wan, Xiang Yu

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2025
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicLight effects on plants
Canadian institutionsInstitute for Biological Sciences
FundersNational Natural Science Foundation of China
KeywordsCircadian rhythmTranscription (linguistics)TranscriptomeTranslation (biology)GeneCircadian clockGene expressionMessenger RNA

Abstract

fetched live from OpenAlex

ABSTRACT Circadian rhythms orchestrate gene expression to align plant growth and development with daily environmental cycles. However, the post-transcriptional mechanisms that coordinate transcriptional and translational rhythmicity remain incompletely understood. To address this, we analyzed time-series transcriptome and translatome profiles in Arabidopsis seedlings, identifying 5,185 genes with rhythmicity at both levels. These genes were classified into four distinct groups based on phase and amplitude differences between transcription and translation. Circadian mRNAs with high oscillation amplitudes tended to undergo co-translational RNA decay (CTRD), whereas intronless genes displayed the lowest amplitudes, likely due to their mRNA instability and short half-lives. While CTRD and NAD⁺ capping modulate amplitude differences, intronless and circadian translational efficiency (TE) influence both phase and amplitude variations. Additionally, CTRD, NAD + capping and circadian TE facilitate fast recovery of heat-induced genes to normal hemostasias. Collectively, our findings demonstrate that these post-transcriptional regulation shapes both synchronized and decoupled transcription and translation during plants response to diel and environmental dynamics.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.893
Threshold uncertainty score0.916

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.193
Teacher spread0.181 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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